介绍了120 t RH炉MFB顶枪在真空室烘烤、去除真空室积渣及吹氧脱碳等方面的应用;考察了真空室预热烘烤煤氧比和流渣煤氧比,得出其最佳值分别为1∶0.75和1∶1.5;讨论了真空脱碳过程中合适的吹氧量,认为[O]质量分数保持在200×10-6以...介绍了120 t RH炉MFB顶枪在真空室烘烤、去除真空室积渣及吹氧脱碳等方面的应用;考察了真空室预热烘烤煤氧比和流渣煤氧比,得出其最佳值分别为1∶0.75和1∶1.5;讨论了真空脱碳过程中合适的吹氧量,认为[O]质量分数保持在200×10-6以上,可以避免对脱碳速度的制约;另外,还讨论了挽救低温钢水事故过程中合适的加铝量及升温效果。展开更多
This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech.The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transfor...This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech.The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transform(FFT) spectral entropy,delta FFT spectral entropy,Mel-frequency filter bank(MFB) spectral entropy,and Delta MFB spectral entropy.Spectral-based entropy features are simple.They reflect frequency characteristic and changing characteristic in frequency of speech.We implement an emotion rejection module using the probability distribution of recognized-scores and rejected-scores.This reduces the false recognition rate to improve overall performance.Recognized-scores and rejected-scores refer to probabilities of recognized and rejected emotion recognition results,respectively.These scores are first obtained from a pattern recognition procedure.The pattern recognition phase uses the Gaussian mixture model(GMM).We classify the four emotional states as anger,sadness,happiness and neutrality.The proposed method is evaluated using 45 sentences in each emotion for 30 subjects,15 males and 15 females.Experimental results show that the proposed method is superior to the existing emotion recognition methods based on GMM using energy,Zero Crossing Rate(ZCR),linear prediction coefficient(LPC),and pitch parameters.We demonstrate the effectiveness of the proposed approach.One of the proposed features,combined MFB and delta MFB spectral entropy improves performance approximately 10% compared to the existing feature parameters for speech emotion recognition methods.We demonstrate a 4% performance improvement in the applied emotion rejection with low confidence score.展开更多
For the magnetized fluidized bed(MFB)with the binary mixture of Geldart-B magnetizable and nonmagnetizable particles,the magnetically induced segregation between these two kinds of particles occurs at high magnetic fi...For the magnetized fluidized bed(MFB)with the binary mixture of Geldart-B magnetizable and nonmagnetizable particles,the magnetically induced segregation between these two kinds of particles occurs at high magnetic field intensities(H),leading to the deterioration of the fluidization quality.The critical intensity(H_(ms))above which such segregation commences varies with the gas velocity(U_g).This work focuses on establishing a segregation model to theoretically derive the H_(ms)–U_g relationship.In a magnetic field,the magnetizable particles form agglomerates.The magnetically induced segregation in essence refers to the size segregation of the binary mixture of agglomerates and nonmagnetizable particles.Consequently,the segregation model was established in two steps:first,the size of agglomerates(d_A)was calculated by the force balance model;then,the H_(ms)–U_g relationship was obtained by substituting the expression of d_Ainto the basic size segregation model for binary mixtures.As per the force balance model,the cohesive and collision forces were 1_2 orders of magnitude greater than the other forces exerted on the agglomerates.Therefore,the balance between these two forces largely determined d_A.The calculated d_A increased with increasing H and decreasing U_g,agreeing qualitatively with the experimental observation.The calculated H_(ms)–U_ g relationship agreed reasonably with the experimental data,indicating that the present segregation model could predict well the segregation behavior in the MFB with the binary mixture.展开更多
文摘介绍了120 t RH炉MFB顶枪在真空室烘烤、去除真空室积渣及吹氧脱碳等方面的应用;考察了真空室预热烘烤煤氧比和流渣煤氧比,得出其最佳值分别为1∶0.75和1∶1.5;讨论了真空脱碳过程中合适的吹氧量,认为[O]质量分数保持在200×10-6以上,可以避免对脱碳速度的制约;另外,还讨论了挽救低温钢水事故过程中合适的加铝量及升温效果。
基金Supported by MIC,Korea under ITRC IITA-2009-(C1090-0902-0046)the Korea Science and Engineering Foundation(KOSEF) funded by the Korea government(MEST)(Grant No.20090058909)
文摘This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech.The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transform(FFT) spectral entropy,delta FFT spectral entropy,Mel-frequency filter bank(MFB) spectral entropy,and Delta MFB spectral entropy.Spectral-based entropy features are simple.They reflect frequency characteristic and changing characteristic in frequency of speech.We implement an emotion rejection module using the probability distribution of recognized-scores and rejected-scores.This reduces the false recognition rate to improve overall performance.Recognized-scores and rejected-scores refer to probabilities of recognized and rejected emotion recognition results,respectively.These scores are first obtained from a pattern recognition procedure.The pattern recognition phase uses the Gaussian mixture model(GMM).We classify the four emotional states as anger,sadness,happiness and neutrality.The proposed method is evaluated using 45 sentences in each emotion for 30 subjects,15 males and 15 females.Experimental results show that the proposed method is superior to the existing emotion recognition methods based on GMM using energy,Zero Crossing Rate(ZCR),linear prediction coefficient(LPC),and pitch parameters.We demonstrate the effectiveness of the proposed approach.One of the proposed features,combined MFB and delta MFB spectral entropy improves performance approximately 10% compared to the existing feature parameters for speech emotion recognition methods.We demonstrate a 4% performance improvement in the applied emotion rejection with low confidence score.
基金Supported by the National Natural Science Foundation of China(21325628)the Major Research Plan of the National Natural Science Foundation of China(91334108)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(YZ201641)
文摘For the magnetized fluidized bed(MFB)with the binary mixture of Geldart-B magnetizable and nonmagnetizable particles,the magnetically induced segregation between these two kinds of particles occurs at high magnetic field intensities(H),leading to the deterioration of the fluidization quality.The critical intensity(H_(ms))above which such segregation commences varies with the gas velocity(U_g).This work focuses on establishing a segregation model to theoretically derive the H_(ms)–U_g relationship.In a magnetic field,the magnetizable particles form agglomerates.The magnetically induced segregation in essence refers to the size segregation of the binary mixture of agglomerates and nonmagnetizable particles.Consequently,the segregation model was established in two steps:first,the size of agglomerates(d_A)was calculated by the force balance model;then,the H_(ms)–U_g relationship was obtained by substituting the expression of d_Ainto the basic size segregation model for binary mixtures.As per the force balance model,the cohesive and collision forces were 1_2 orders of magnitude greater than the other forces exerted on the agglomerates.Therefore,the balance between these two forces largely determined d_A.The calculated d_A increased with increasing H and decreasing U_g,agreeing qualitatively with the experimental observation.The calculated H_(ms)–U_ g relationship agreed reasonably with the experimental data,indicating that the present segregation model could predict well the segregation behavior in the MFB with the binary mixture.
